In medical imaging, accurate diagnosis of disease often depends on the detection of small, low-contrast details within the image. The probability of detection of these details, in turn, depends critically on their visibility in the image. A number of factors, including the body part being imaged, the exposure geometry and technique, and the characteristics of the detector can affect this visibility. In traditional screen/film radiography, for example, the characteristic curve of the screen/film system largely determines the contrast with which details are displayed in the final image. In digital radiography, on the other hand, the separation of image acquisition and display stages allows an image or portion of an image to be displayed at an arbitrary output contrast depending on the particular need. This is done by creating a look-up table (LUT) that maps the digital values of the acquired image into a new set of digital values that will be sent to the display device and written on some output medium (e.g., on film or on a CRT). In addition, other kinds of image processing are often used to try to enhance even more the visibility of various structures in medical images. For example, edge enhancement techniques can be used to increase the visual contrast of edges, and therefore the conspicuity of certain kinds of structures in an image.
The effectiveness of such image processing techniques depends on the careful choice of the various parameters that control their performance. Furthermore, the variety of exam types and diagnostic needs encountered in medical imaging generally requires that such parameters be chosen based on the image to be processed, rather than based on some set of fixed characteristics applicable to every image. For example, histogram-based tone-scale transformation is a simple and effective way of adjusting the contrast of an image. However, the histogram is a global characteristic of the image, and therefore does not distinguish between the desired or important regions of the image (e.g., the body part or portion being imaged) and the undesired or unimportant regions of the image (e.g., regions of direct x-ray background or areas of very low exposure behind collimator blades used to restrict the size of the irradiation field). Thus, a tone-scale transformation based on such a histogram will be suboptimum if it is unduly influenced by the unimportant regions of the image.
Even in image processing techniques that are locally adaptive within the image, i.e., that depend on the local context of the image, such unimportant regions can create unwanted effects. For example, in adaptive edge enhancement, some parameters of the algorithm often depend on the global histogram or on a local histogram (Sezan et al., IEEE Trans. Med. Imaging, vol. 8, p. 154, 1989). If this histogram contains data from the undesired regions of the image, the algorithm parameters will be negatively influenced and a suboptimum edge enhancement performance can be expected.
Thus, it is desirable to provide a method to detect and distinguish the undesired and desired regions of a digital radiograph. Such a method would allow the parameters for various image processing techniques to be calculated more easily. In addition, the values of these parameters would be close to the optimum values, leading to improved quality and better depiction of the information needed for an accurate diagnosis.
A variety of methods have been proposed to detect undesired regions in images. In the descriptions that follow, the word "background" will be used to denote the direct x-ray background, that is, the region around the body part being imaged that has received unattenuated x-rays. The word "foreground" will be used to denote highly (x-ray) absorbing structures, like collimator blades, used to define the size and shape of the irradiation field. Thus, a digital radiograph consists of three regions: the body part being imaged (the object), the background, and the foreground.
Histogram-based methods for detecting foreground and/or background usually depend on the detection and recognition of peaks in the histogram (see Sezan et al., IEEE Trans. Med. Imaging, vol. 8, p. 154, 1989). For example, U.S. Pat. No. 4,731,863, issued Mar. 15, 1988, inventors Sezan et al., teaches a technique for finding gray level thresholds between anatomical structures and image regions based on zero-crossings of a peak detection function derived from application of smoothing and differencing operators to the image histogram. This method produces a series of peaks by analyzing the histogram at several different signal resolutions. One problem with this method is that the peaks need to be interpreted for each exam type and exposure technique. That is, a low-signal peak could correspond to foreground, but it could also correspond to a body portion if no foreground is present in the image. Correspondingly, a high-signal peak may be the image background, or it may be a high-signal body portion. Thus, some additional information may be needed to make the method robust.
Other methods of histogram analysis have also been proposed. U.S. Pat. No. 4,952,805, issued Aug. 28, 1990, inventor Tanaka, teaches a foreground finding technique based on dividing the histogram into several sections with an intensity thresholding procedure and then doing a statistical shape analysis (discriminate analysis) of the section believed to correspond to foreground. A decision about the presence and extent of a foreground region is made based on the shape of this section of the histogram. However, as above, the large variety of histogram shapes that can occur with different exam types and different input modalities (such as magnetic resononance imaging (MRI), computed tomography (CT), ultrasound (US), nuclear medicine, digital subtraction angiography (DSA), computed radiography (CR)) make this type-of analysis subject to error. In addition, since a single threshold is chosen to represent the transition from foreground to object, this method does not perform well when the transition is fairly wide, such as when x-ray scatter is present.
European Patent Application 288,042, published Oct. 26, 1988, inventors Tanaka et al., proposes a foreground and background finding method using the image histogram. In this method, the histogram is again divided into a number of sections by an automatic thresholding procedure. Then a statistical analysis (discriminate analysis), combined with information about the exam type, exposure technique, and desired body portion to be displayed, is used to adjust the separation points between the sections until desired ranges for the foreground, object, and background regions are found. This method is less prone to variations in exam type and input modality because this information is incorporated into the decision process. However, the use of fixed thresholds still poses problems if there is nonuniformity in either the foreground or background.
Generally, histogram-based methods work best when the peaks in the histogram corresponding to foreground and background are far enough away from the peak(s) corresponding to the body part of interest that they can be identified as separate. If the peaks overlap, which can occur if the background or foreground are nonuniform across the image, or if scatter behind the collimators causes the signal level in that area to come close to the highly x-ray absorbing structures in the body, then it becomes more difficult to separate foreground/background regions in the histogram with a single threshold.
A more effective way of detecting foreground and background is to include spatial information about the image in the analysis, in addition to the intensity information provided by the histogram. Several methods have been described for doing this. U.S. Pat. No. 4,804,842, issued Feb. 14, 1989 inventor Nakajima, and U.S. Pat. No. 5,028,782, issued Jul. 2, 1991 inventor Nakajima, for example, disclose a method for detecting foreground in an image based on calculating derivatives of the input image and then identifying edge points as those points where the value of the derivative is higher than a threshold value. Then a new histogram of the input image is done using only the points identified as edge points, and from this histogram a threshold value is chosen to represent the boundary of the foreground (i.e., the irradiation field). This method is claimed to provide a more accurate measure of the field than a simple histogram method. However, it still requires an a priori knowledge that a collimator or field stop was in fact used to define the irradiation field, otherwise low-signal portions of the image inside the body part may be clipped by the intensity thresholding that defines the boundary. Furthermore, if image pixels inside the body part have a signal value comparable to or lower than those underneath the collimator region (as when there is significant x-ray scatter), the edge of the irradiation field may not even be found with this method. Finally, if the region under the collimator is nonuniform in intensity, which is frequently the case when there is scatter present, there will not be a strong edge at the boundary of the irradiation field, and the derivative at the edge points may not have a high enough value to pass the threshold, leading to inaccuracies in finding the edge.
Other foreground detection methods have been described that use one dimensional edge detection along arbitrary lines drawn across the image. For example, U.S. Pat. No. 4,967,079, issued Oct. 30, 1990, inventor Shimura, discloses a method for storage phosphor digital radiography systems that uses derivatives along radial lines from the image center, followed by a thresholding operation to detect potential edge points of the irradiation field. The boundary of the field is recognized by testing the colinearity of the found edge points. In order to be effective, this method requires a strong edge transition from object to foreground or from background to foreground. While the latter is generally true, the object to foreground transition can sometimes be very weak and even inverted (body part with a lower signal than foreground, due to scatter). Furthermore, if the image involves multiple smaller images recorded on a large detector (so-called subdivision or multiple exposure recording), there will be many edges detected along radial lines from the image center, possibly leading to the detection of false boundaries.
An alternate approach to foreground detection has been disclosed in U.S. Pat. No. 4,859,850, issued Aug. 22, 1989, inventor Funahashi. In this case, lines are extended from the edge of the image towards the center and, for each line, the transition regions from low signal to high signal at the edge of an irradiation field are fit with a linear or nonlinear equation. When the differences between the extrapolated fitted values (calculated from the equation) and the actual image values inside the field become too large, or when the extrapolated values reach a threshold signal level, the edge of the field is assumed to have been found. One problem with this method is that it assumes that collimation has been used (i.e., a priori knowledge of the exposure technique is required). A second problem has already been mentioned above, namely, that the method assumes that the signal values inside the irradiation field are always larger than those immediately outside it, which is not always the case when scatter is present. A third problem is that if subdivision recording has been used, the method may not find all of the necessary edges to define each irradiation field within the image. Finally, the use of multiple linear or nonlinear fits on multiple lines across the image is an inefficient, time-consuming way to find the field boundaries.
A possible solution to the previously mentioned problem of detecting edges in subdivision recording has been proposed in U.S. Pat. No. 4,851,678, issued Jul. 25, 1989, inventors Adachi et al. In this method, designed for storage phosphor digital radiography, potential edge points can still be found using the above method of differentiation along lines, but other possibilities are also disclosed. For example, once a few candidate edge points have been found, a boundary tracking procedure, based on following the likeliest edge points around the boundary from nearest neighbor to nearest neighbor (using a ridge-following algorithm) until they close on themselves again, is used to find the irradiation field. This method claims to handle multiple exposure fields as well since multiple starting edge points can each be followed around their respective irradiation fields. A third method using statistical template matching with a series of stored, commonly used subdivision patterns is also disclosed. Since the method of finding prospective edge points is similar to those above, similar potential problems exist, namely, that the method can break down when the edge transition from object to foreground is weak or inverted. Also the ridge-following algorithm can be very sensitive to noise, so the image data must be smoothed before the analysis.
As indicated above, the presence of multiple smaller images recorded on a single larger recording medium (i.e., subdivision recording) can create problems in locating all of the foreground in the image. Sometimes a preprocessing stage can be used to identify the use of subdivision recording and also the format of the image (2-on-1, 4-on-1, etc.). For example, U.S. Pat. No. 4,829,181, issued May 9, 1989, inventor Shimura, teaches a method of recognizing a subdivision pattern in a storage phosphor system using differentiation to detect prospective edge points, followed by a colinearity test to see if the edge points lie on straight lines. If the edge points lie on straight lines, subdivision recording is judged to be present. A limitation of this method is that it can detect only rectilinear patterns, i.e., patterns with essentially horizontal or vertical linear separations.
Another approach to detecting such subdivision patterns is the use of pattern matching. U.S. Pat. No. 4,962,539, issued Oct. 9, 1990, inventor Takeo et al., discloses a method that uses a set of binary, stored masks representing typical subdivision recording patterns. The input digital image is converted into a binary image by thresholding, and the resulting binary image is statistically compared With each of the masks in the stored set. The stored mask with the highest degree of matching is judged to be the recording pattern on the input image. While this method can handle a wider variety of patterns than the one above, it is still limited to the stored library of patterns for matching. Any irregular patterns not included in the library may not have a high degree of matching, and may therefore be chosen incorrectly. Furthermore, the statistical matching can be complex and time consuming.